Six dimensional tracking of sparse ladar data

ABSTRACT

An apparatus, and method of operating the same processes of LADAR data including iterating back and forth between target detection in a 2-D array having range and range rate dimension, and a 4-D array having azimuth, azimuthal velocity, elevation, &amp; elevation velocity dimensions. The apparatus includes a receiver and a processor arranged to generate photo events including target signal photo events and background photo events, transform the photo events into the 2-D target tracking array including range and range-rate parameters and tag photo events determined to be 2-D target signal photo events. The processor transforms tagged photo events into the 4-D target and tags photo events determined to be 4-D target signal photo events.

TECHNICAL FIELD

The subject disclosure relates to laser radar systems and, moreparticularly to processing sparse LADAR data.

BACKGROUND

Laser Detection and Ranging (LADAR), also referred to as LIDAR or LiDAR,is a laser-based radar technology used to capture high-resolutionimaging information and to measure distances by illuminating a targetobject or terrain with laser light. LADAR has been used to create highresolution survey maps of geographic areas and detailed 3-D images ofobjects. More recently, LADAR has been implemented to support controland navigation of autonomous cars. LADAR uses ultraviolet, visible, ornear infrared light to image objects or terrains. Using a narrow laserbeam, a LADAR system can detect physical features of objects withextremely high resolutions.

A rigid target moving with a constant 3-D velocity has a fixedrelationship with its component range, range rate, azimuth, azimuthrate, elevation, and elevation rate. In order to find where the rigidtarget is, a LADAR system must detect and determine a common value foreach of the target's range, range rate, azimuth, azimuth rate,elevation, and elevation rate for all of the points comprising thetarget. Existing LADAR tracking target detection may ignore subsets ofthese dimensions, creating slower processing times, range ambiguity,inefficient background rejection, lack of motion compensation, andinability to simultaneously detect, track, and motion compensatemultiple targets.

SUMMARY OF THE TECHNOLOGY

The application describes exemplary tracking and motion compensationsystems and methods for iterating between target detection in twodimensions, including range and range rate dimensions, and fourdimensions, including azimuth, azimuthal velocity, elevation, &elevation velocity dimensions. This detection technique may leverage thebinary nature of Geiger-Mode Avalanche Photodiode data (GMAPD) toperform target acquisition using sparse operations, and resolving rangeambiguities by utilizing information regarding the nature of atransmitted waveform. The subject detection technique may perform sixdimensional state-space target segmentation, wherein the target data ismotion compensated upon acquisition and if multiple targets are presentin the data, the tracking and motion compensation systems and methodscan simultaneously detect, track, and motion compensate each one.

An example laser and detection and ranging system includes a receiverarranged to receive scattered laser pulses. The laser and detection andranging system includes a processor. The processor is arranged togenerate photo events based on the received scattered laser pulses, as afirst step. The photo events include target signal photo events andbackground photo events. The processor is arranged to transform thephoto events into a two dimensional 2-D target tracking array includingrange and range-rate parameters, discard photo events determined to bebackground photo events, and tag photo events determined to be 2-Dtarget signal photo events, as a second step. The processor is arrangedto transform photo events tagged as 2-D target signal photo events intoa four dimensional 4-D target tracking array including azimuth,azimuthal velocity, elevation, and elevation velocity parameters,discard photo events determined to be background photo events, and tagphoto events determined to be 4-D target signal photo events, as a thirdstep. The example laser detection and ranging system may include one ormore of the following steps, either alone or in combination.

The processor may repeat the second and third steps for a plurality ofiterations. After the first iteration, the photo events transformed inthe second step are the tagged 4-D target signal photo events from thethird step of the previous iteration. The parameters associated witheach of the tagged 4-D target signal photo events may be stored in amemory. The parameters stored in a memory may represent a sixdimensional (6D) array. The parameters may include range, range-rate,azimuth, azimuthal velocity, elevation, and elevation velocity.

The processor may determine that photo events with a signal strengthlower than a detection threshold are the background photo events. Thedetection threshold may include a statistically significant photo event.The detection threshold applied in the second step may be different thanthe detection threshold applied in the third step.

The laser detection and ranging system may include a laser transmitterarranged to emit laser pulses toward a target. The photo events mayinclude sparse video data.

An example method for laser detection and ranging includes receivingscattered laser pulses, as a first step. The method includes generatingphoto events based on the received scattered laser pulses, as a secondstep. The photo events include target signal photo events and backgroundphoto events. The method includes transforming the photo events into atwo dimensional 2-D target tracking array including range and range-rateparameters, discarding photo events determined to be background photoevents, and tagging photo events determined to be 2-D target signalphoto events, as a third step. The method includes transforming photoevents tagged as 2-D target signal photo events into a four dimensional(4-D) target tracking array including azimuth, azimuthal velocity,elevation, and elevation velocity parameters, discarding photo eventsdetermined to be background photo events, and tagging photo eventsdetermined to be 4-D target signal photo events, as a fourth step. Theexample method for laser detection and ranging may include one or moreof the following steps, either alone or in combination.

The method may include repeating the third and fourth steps for aplurality of iterations. After the first iteration, the photo eventstransformed in the third step may be the tagged 4-D target signal photoevents from the fourth step of the previous iteration.

The method may include storing the parameters associated with each ofthe tagged 4-D target signal photo events in a memory. The parametersmay be stored in memory to represent a six dimensional (6D) array. Theparameters may include range, range-rate, azimuth, azimuthal velocity,elevation, and elevation velocity.

Discarding photo events determined to be background photo events mayinclude comparing the photo events signal strength, wherein photo eventshaving a signal strength lower than a detection threshold are thebackground photo events. The detection threshold may include astatistically significant photo event. The detection threshold appliedin the third step may be different than a detection threshold applied inthe fourth step.

An example LADAR sparse state-space carving system includes a receiver.The receiver is arranged to receive scattered laser pulses and generatephoto events based on the received scattered laser pulses. The photoevents include target signal photo events and background photo events.The system includes a two dimensional 2-D target tracking detectorarranged to transform the photo events into a 2-D target tracking arrayincluding range and range-rate parameters, discard photo eventsdetermined to be background photo events, and tag photo eventsdetermined to be 2-D target signal photo events. The system includes afour dimensional 4-D target tracking detector arranged to transformphoto events tagged as 2-D target signal photo events into a 4-D targettracking array including azimuth, azimuthal velocity, elevation, andelevation velocity parameters, discard photo events determined to bebackground photo events, and tag photo events determined to be 4-Dtarget signal photo events. The example LADAR sparse state-space carvingsystem may include one or more of the following steps, either alone orin combination.

The 2-D target tracking detector and the 4-D target tracking detectormay transform the photo events iteratively over a plurality ofiterations. After the first iteration, the photo events input into andtransformed by the 2-D target tracking detector may be the tagged 4-Dtarget signal photo events from the 4-D target tracking detector duringa previous iteration.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary laser detection and ranging(LADAR) system.

FIG. 2 is a block diagram of a computer system arranged to performprocessing associated with a laser detection and ranging system.

FIG. 3 is a block diagram of a six-dimensional tracking system arrangedto perform processing associated with laser detection and ranging systemdata.

FIG. 4 is an example set of simulated video data including backgroundphoto-events and signal photo events.

FIG. 5 is the example set of simulated video data projected into a heatmap illustrating cross-correlation values as a function of velocity andrange of a simulated target.

FIG. 6 is the example set of simulated video data projected into afour-dimensional array, illustrated as a slice through two of thedimensions containing a peak.

FIG. 7 is the example set of simulated video data showing background anda correctly localized target after one iteration of the subjecttechnology.

FIG. 8 is a motion compensated focal plane array image of the exampleset of simulated video data after two iterations of the subjecttechnology.

DETAILED DESCRIPTION

The subject technology addresses deficiencies associated with detectionof dim signals in LADAR data. The application includes exemplarydevices, systems, and methods for efficient processing of LADAR dataincluding iterating back and forth between target detection in atwo-dimensional array (range and range rate, herein referred to as R and{dot over (R)} respectively) and a four-dimensional array (azimuth,azimuthal velocity, elevation, & elevation velocity, herein referred toas ϕ, {dot over (ϕ)}, θ, and {dot over (θ)}, respectively).

FIG. 1 is a block diagram of an exemplary LADAR system 100. System 100includes a laser transmitter 102, a processor 104, and a receiver 106.Laser transmitter 102 is configured to emit laser pulses 108 whilereceiver 106 is configured to receive reflected and/or returned laserpulses 110 scattered from a target object and/or terrain. Processor 104may perform functions such as, without limitation, streamingcross-correlations, artifact corrections, target acquisitions, andtracking and discrimination of targets. Processor 104 may generate imagedata and/or information for other systems such as an automatic targetrecognizer (ATR) system or a combat system/network such as AEGIS.

FIG. 2 is block diagram of a computer system 200 arranged to performprocessing associated with a LADAR system such as, for example, system100, 300. The exemplary computer system 200 includes a centralprocessing unit (CPU) 202, a memory 204, and an interconnect bus 206.The CPU 202 may include a single microprocessor or a plurality ofmicroprocessors or special purpose processors for configuring computersystem 200 as a multi-processor system. The memory 204 illustrativelyincludes a main memory and a read only memory. The computer 200 alsoincludes the mass storage device 208 having, for example, various diskdrives, tape drives, etc. The memory 204 also includes dynamic randomaccess memory (DRAM) and high-speed cache memory. In operation, memory204 stores at least portions of instructions and data for execution bythe CPU 202. The memory 204 may also contain compute elements, such asDeep In-Memory Architectures (DIMA), wherein data is sent to memory anda function of the data (e.g., matrix vector multiplication) is read outby the CPU 202.

The mass storage 208 may include one or more magnetic disk, optical diskdrives, and/or solid state memories, for storing data and instructionsfor use by the CPU 202. At least one component of the mass storagesystem 208, preferably in the form of a non-volatile disk drive, solidstate, or tape drive, stores the database used for processing data andcontrolling functions of a LADAR system 100, 300. The mass storagesystem 208 may also include one or more drives for various portablemedia, such as a floppy disk, flash drive, a compact disc read onlymemory (CD-ROM, DVD, CD-RW, and variants), memory stick, or anintegrated circuit non-volatile memory adapter (i.e. PC-MCIA adapter) toinput and output data and code to and from the computer system 200.

The computer system 200 may also include one or more input/outputinterfaces for communications, shown by way of example, as interface 210and/or a transceiver for data communications via the network 212. Thedata interface 210 may be a modem, an Ethernet card or any othersuitable data communications device. To provide the functions of aprocessor according to FIG. 1, the data interface 210 may provide arelatively high-speed link to a network 212, such as an intranet,internet, Aegis network, or the Internet, either directly or throughanother external interface. The communication link to the network 212may be, for example, optical, wired, or wireless (e.g., via satellite orcellular network). The computer system 200 may also connect via the datainterface 210 and network 212 to at least one other computer system toperform remote or distributed multi-sensor processing related to, forexample, a common operational picture (COP). Alternatively, the computersystem 200 may include a mainframe or other type of host computer systemcapable of Web-based communications via the network 212. The computersystem 200 may include software for operating a network application suchas a web server and/or web client.

The computer system 200 may also include suitable input/output ports,that may interface with a portable data storage device, or use theinterconnect bus 206 for interconnection with a local display 216 andkeyboard 214 or the like serving as a local user interface forprogramming and/or data retrieval purposes. The display 216 may includea touch screen capability to enable users to interface with the system200 by touching portions of the surface of the display 216. Serveroperations personnel may interact with the system 200 for controllingand/or programming the system from remote terminal devices via thenetwork 212.

The computer system 200 may run a variety of application programs andstore associated data in a database of mass storage system 208. One ormore such applications may include a video filter array such asdescribed with respect to FIGS. 4, 5, 6, 7 and 8.

The components contained in the computer system 200 may enable thecomputer system to be used as a server, workstation, personal computer,network terminal, mobile computing device, mobile telephone, System on aChip (SoC), and the like. As discussed above, the computer system 200may include one or more applications such as waveform control, streamingcross-correlations, artifact corrections, target acquisitions, and thetracking and discrimination of targets. The system 200 may includesoftware and/or hardware that implements a web server application. Theweb server application may include software such as HTML, XML, WML,SGML, PHP (Hypertext Preprocessor), CGI, and like languages.

The foregoing features of the disclosure may be realized as a softwarecomponent operating in the system 200 where the system 200 includes Unixworkstation, a Windows workstation, a LINUX workstation, or other typeof workstation. Other operation systems may be employed such as, withoutlimitation, Windows, MAC OS, and LINUX. In some aspects, the softwarecan optionally be implemented as a C language computer program, or acomputer program written in any high level language including, withoutlimitation, Javascript, Java, CSS, Python, Keras, TensorFlow, PHP, Ruby,C++, C, Shell, C #, Objective-C, Go, R, TeX, VimL, Perl, Scala,CoffeeScript, Emacs Lisp, Swift, Fortran, or Visual BASIC. Certainscript-based programs may be employed such as XML, WML, PHP, and so on.The system 200 may use a digital signal processor (DSP).

As stated previously, the mass storage 208 may include a database. Thedatabase may be any suitable database system, including the commerciallyavailable Microsoft Access database, and can be a local or distributeddatabase system. A database system may implement Sybase and/or a SQLServer. The database may be supported by any suitable persistent datamemory, such as a hard disk drive, RAID system, tape drive system,floppy diskette, or any other suitable system. The system 200 mayinclude a database that is integrated with system 100, 300, however, itwill be understood that, in other implementations, the database and massstorage 208 can be an external element.

In certain implementations, the system 200 may include an Internetbrowser program and/or be configured operate as a web server. In someconfigurations, the client and/or web server may be configured torecognize and interpret various network protocols that may be used by aclient or server program. Commonly used protocols include HypertextTransfer Protocol (HTTP), File Transfer Protocol (FTP), Telnet, andSecure Sockets Layer (SSL), and Transport Layer Security (TLS), forexample. However, new protocols and revisions of existing protocols maybe frequently introduced. Thus, in order to support a new or revisedprotocol, a new revision of the server and/or client application may becontinuously developed and released.

In one implementation, the system 100 or 300 includes a networked-based,e.g., Internet-based, application that may be configured and run on thesystem 200 and/or any combination of the other components of the system100 or 300. The computer system 200 may include a web server running aWeb 2.0 application or the like. Web applications running on system 100or 300 may use server-side dynamic content generation mechanisms such,without limitation, Java servlets, CGI, PHP, or ASP. In certainimplementations, mashed content may be generated by a web browserrunning, for example, client-side scripting including, withoutlimitation, JavaScript and/or applets on a wireless device.

In certain implementations, system 100, 200, and/or 300 may includeapplications that employ asynchronous JavaScript+XML (Ajax) and liketechnologies that use asynchronous loading and content presentationtechniques. These techniques may include, without limitation, XHTML andCSS for style presentation, document object model (DOM) API exposed by aweb browser, asynchronous data exchange of XML data, and web browserside scripting, e.g., JavaScript. Certain web-based applications andservices may utilize web protocols including, without limitation, theservices-orientated access protocol (SOAP) and representational statetransfer (REST). REST may utilize HTTP with XML.

The system 100, 300, computer system 200, or another component of system100 may also provide enhanced security and data encryption. Enhancedsecurity may include access control, biometric authentication,cryptographic authentication, message integrity checking, encryption,digital rights management services, and/or other like security services.The security may include protocols such as IPSEC and IKE. The encryptionmay include, without limitation, DES, 3DES, AES, RSA, ECC, and any likepublic key or private key based schemes.

FIG. 3 is a block diagram of a six-dimensional tracking system 300arranged to perform processing associated with laser detection andranging system data. Video input data 302 may be transformed into thesix-dimensional tracking system 300. The video input data 302 mayinclude target photo events and background photo events, such that thevideo data 302 may be derived from a LADAR dwell of a target and thetarget surroundings. Video data 302 may be derived from received orscattered laser pulses, the laser pulses transmitted by a LADARtransmitter or the like. The video data 302 may include sparse andbinary video data such as Geiger-Mode Avalanche Photodiode data (GMAPD).

In certain implementations, 2-D target tracking detector 304 maythereafter receive video data 302. 2-D target tracking detector 304 maybe operated by use of processor 104 or exemplary computer system 200.The 2-D target tracking detector 304 may be configured to concurrentlydetermine the range and the range-rate (i.e., velocity) of photo eventswithin video data 302 based on transmissions of laser pulses, andreceived laser pulses such as return times of photons. Henceforth, theterms speed, velocity, and range-rate refer to the velocity of thetarget relative to the exemplary LADAR system 100 along the range axis(i.e., the line/direction connecting the exemplary LADAR system 100 andthe target). The 2-D target tracking detector 304 may accuratelydetermine these target characteristics despite complex scattering of thetransmitted light, imperfect detection of the returns, unwanteddetections due to ambient light and electrical noise, modulation of thereturn due to target motion, and/or other practical complications andlimitations.

In some implementations, the 2-D target tracking detector 304 scales(e.g., stretches or compresses) the transmit times of emitted laserpulses according to a plurality of hypothesized and/or predictedvelocities and, for each hypothesized velocity, computes across-correlation of the scaled transit times with the return times ofdetection events, and identifies the peak cross-correlation power valuefor the plurality of hypothesized/trial velocities. Determining thetemporal scaling that yields the highest correlation peak value allowsthe 2-D target tracking detector 304 to concurrently (e.g.,simultaneously) determine both the range and range-rate of photo events.An example 2-D target tracking detector that determines both the rangeand range-rate of photo events is described in U.S. patent applicationSer. No. 16/863,064 (Greenberg & Marcus) entitled “SYSTEM AND METHOD FORDETERMINING RANGE-RATE AND RANGE EXTENT OF A TARGET”. The content ofU.S. patent application Ser. No. 16/863,064, particularly the contentrelated the process of target acquisition (e.g., FIG. 3 of U.S. patentapplication Ser. No. 16/863,064 and the accompanying description), isincorporated herein by reference.

In some implementations, the 2-D target tracking detector 304 maycalculate the range and the range-rate of the target in video data 302based on a plurality of cross-correlation power values, wherein the 2-Dtarget tracking detector 304 identifies a peak cross-correlation powervalue (e.g., the highest cross-correlation value) from among theplurality of cross-correlation power values and determines the pair-wisedifference value associated with the peak cross-correlation power value.

After determining the range and range-rate of the target within videodata 302, or attempting to find at least one peak correlating to astatistically significant result within a two-dimensional array withrange and range-rate dimensions, video data 302 may thereafter betransmitted to 4-D target tracking detector 306. Statisticallysignificant results may reach a prescribed threshold of counts. Photoevents associated with every range and range rate detection within videodata 302 may be applied to the 4-D target tracking detector 306. 4-Dtarget tracking detector 306 may cross-range filter the photo events,accounting for both focal plane position and focal plane motion over aLADAR dwell. Video data not associated with detections may be discardedor otherwise rejected before the video data is exported to 4-D targettracking detector 306.

In some implementations, 4-D target tracking detector 306 may beoperated by use of processor 104 or exemplary computer system 200. Theprocessor 104 or exemplary computer system 200 may be arranged togenerate a video filter array, the video filter array (VFA) including aset of estimated velocity pixel coordinate components arranged in alinear data set while representing a plurality of two-dimensional arraysassociated with a plurality of frames. The VFA may be stored in memory204 and/or mass storage 208. Each of the plurality of two-dimensionalarrays may have dimensions equal to dimensions of the focal plane arrayof the receiver 106, and generate a plurality of detected photo eventsbased on received scattered laser pulses or video data. The 4-D targettracking detector 306 may also filter the plurality of photo eventstransmitted to it by linearly indexing each of the plurality of detectedphoto events based on, for each detected photo event, a verticalposition in the focal plane array, a horizontal position in the focalplane array, a frame number, and the dimensions of the focal-planearray. The 4-D target tracking detector 306 may map each detected photoevent to a set of estimated velocity pixel coordinate components basedon a time between receiving the scattered laser pulses and thefocal-plane array vertical and horizontal positions of each of thedetected photo events. In return, the 4-D target tracking detector 306may generate a motion-compensated image associated with the mappedplurality of detected photo events in a filtered two-dimensional arrayhaving dimensions equal to the dimensions of the focal plane array.Further details regarding an implementation of a 4D tracker aredescribed in co-pending U.S. patent application Ser. No. ______,entitled “VIDEO-TRACKING OF SPARSE GEIGER-MODE DATA”, filed on Dec. 30,2020, particularly the content related to generating a video filterarray and using a video filter array with Geiger-mode video data (e.g.,FIGS. 5-8 and the accompanying description) are incorporated herein byreference.

As a result, four-dimensional filtered and motion compensated focalplane array images may be generated. Six dimensional tracking system 300may thereafter associate each four dimensional detection with the videodata that comprises it, and all other video data may be disposed of orotherwise rejected. With the four dimensional detection video data,six-dimensional tracking system 300 may iterate 308, wherein the fourdimensional detection video data is applied to 2-D target trackingdetector 304, and subsequently 4-D target tracking detector 306. Sixdimensional tracking system 300 may iterate 308 multiple times. Sixdimensional tracking system 300 may, in some implementations, refrainfrom iterating 308 entirely. The resulting video data 310 may thereafterbe transmitted, exported, or the like to another system for furtherprocessing.

FIG. 4 is an example set of simulated video data 302, 400 includingbackground photo-events and signal photo events. The example set ofsimulated video data 400 includes video data of a low-signal targetmoving in range and cross-range, for which the range-location was knowna priori to lie between 6 kilometers and 8 kilometers from LADAR system100, as illustrated in range histogram 402. The example set of simulatedvideo data 400 includes roughly 740,000 background photo events androughly 35-50 signal photo events. The signal photo events are notdetectable to a statistically significant degree in a focal plane arrayimage 404 with x and y orthogonal dimensions. Neither are signal photoevents detectable to a statistically significant degree when all videodata is projected onto range histogram 402 with range and countdimensions. This may be the result of the target having a velocity in agiven direction, such that the sparse number of signal photo events arespread out in space and would not yield a statistically significant peakin projected video data. Statistically significant results may reach aprescribed threshold of counts.

FIG. 5 is the example set of simulated video data of FIG. 4 projectedinto a heat map 500 illustrating cross-correlation values as a functionof velocity and range of a simulated target. The heat map 500illustrates minor peaks and troughs, which may be due to backgroundnoise and other undesirable effects, and a series of major peaks 504,which representing statistically significant photo events, somepotentially of an intended target. The many peaks and troughs may resultfrom stochastic noise, whereby it is possible to receive a sequence ofbackground photons/events that happen to line up with some of thetransmit times, and thus generate local peaks in the cross-correlationpower values. As the target's velocity changes, the location of thepeaks in the heat map 500 may move up or down (to reflect a higher orlower velocity), however, the magnitude of the peak will not change.

In light of the example set of simulated video data in FIG. 5, in someimplementations, the LADAR system 100 is capable of transforming rawsensor or video data (e.g., photon detection events), into a space orarray where targets manifest as peaks in a signal such as heat map 500.This transformation may be performed in a streaming manner, which allowsfor up-to-date real-time knowledge of a target's dynamical state, with aconstant latency. Despite the fact that a target's range-rate is notknown in advance in application of the subject technology, the LADARsystem 100 may utilize the fact that the target range-rate has apredictable effect on return timing to estimate the range-rate viahypothesis and test. That is, the LADAR system 100, particularly 2-Dtarget tracking detector 304, applies a series of cross-correlations tothe return data, each against the transmit waveform scaled per adifferent hypothesized range-rate. The LADAR system 100 may alsoleverage a baseline sparse cross-correlation algorithm to rapidlycalculate each row of a two dimensional array parametrized by range andrange-rate. In this transformed space, a moving target presents as asingle peak, for which the range and range-rate can be extracted. Thistarget acquisition process according to some embodiments can work withany transmit waveform, and can therefore be combined with optimal pulseposition modulation waveforms in range-ambiguous scenarios. Furthermore,because the LADAR system 100 can estimate both state variablessimultaneously, it can detect high-speed, low-SNR targets that would beundetectable with any two-step method.

Projecting video data, in the implementation described above, into arange and range rate two-dimensional array may return severalstatistically significant photo events 504. The statisticallysignificant photo events 504 may be statistically significant because anumber of photo events were detected at the respective range andrange-rate such that a target may exist therein. Statisticallysignificant results may reach a prescribed threshold of counts. Forexample, within the example video data, peak 506 was determined to bemost statistically significant, projected into a two-dimensional array.A 2-D target tracking detector 304 would, in return, determine peak 506to include target photo events. Although, because of the nature of theexample data such that it comprises roughly 740,000 background photoevents and roughly 35-50 signal photo events, projecting the video datainto a two-dimensional array having range and range rate dimensionsyielded several statistically significant photo events 504, and as suchpeak 506 may not actually consist of target photo events.

As a result, in some implementations of the subject technology, photoevents of video data 302 that caused peaks 504 may be retrieved from theraw video data and passed from the 2-D target tracking detector 304 tothe 4-D target tracking detector 306. A portion of photo events of videodata 302, not corresponding to peaks 504 may be discarded or otherwiserejected.

FIG. 6 is the example set of simulated video data after projection into4-D target tracking detector 306. The figure shows two-dimensionalslices through the 4-D space, referred to herein as motion compensatedimages 604, correlated to peaks 504 after projection into 2-D targettracking detector 304.

The 4-D target tracking detector 306 illustrates cross-correlationvalues as a function of azimuth, azimuth rate, elevation, and elevationrate on a two-dimensional heat map. As such, the 4-D target trackingdetector 306 may implement Sparse Video Tracking, explained in furtherdetail above. When photo events associated with every range andrange-rate detection are passed from the 2-D target tracking detector304 to the 4-D target tracking detector 306, the photo events areprojected into a four-dimensional space and/or array having azimuth,azimuth rate, elevation, and elevation rate orthogonal dimensions.

It is noteworthy that peak 606 of azimuth, azimuth rate, elevation, andelevation rate space does not correlate to the most statisticallysignificant peak 506 in range and range-rate space. Peak 506, having astatistically significant culmination of photo events in range andrange-rate space, became diffuse in azimuth, azimuth rate, elevation,and elevation rate space. As such, peak 506 may not represent a target.A target should portray a statistically significant culmination of photoevents in azimuth, azimuth rate, elevation, and elevation rate spacealong with range and range-rate space. Statistically significant resultsmay reach a prescribed threshold of counts.

After 4-D target tracking detector 306 projects and extractsstatistically significant photo events 606 correlating to proposedtarget photo events in four dimensions, an output 310 may be generated,the output including the significant photo events 606 such that thesignificant photo events 606 are a hypothesis and/or prediction oftarget signal photons or video data containing hypothesized and/orpredicted target photo events. Alternatively, 4-D target trackingdetector may iterate 308 photo events corresponding to peak 606. Aportion of photo events 302, not corresponding to peak 606 may bediscarded or otherwise rejected.

FIG. 7 is a heat map 700 of the example set of simulated video datashowing a correctly localized target 704 after an iteration 308 oftwo-dimensional and four-dimensional target tracking. Background events702 were retained as a side effect of range ambiguity. Background events702 may be discarded upon another iteration 308 of two-dimensional andfour-dimensional target tracking.

FIG. 8 is a motion compensated focal plane array image 802 having anx-axis 804 and y-axis 806 illustrating 48 photo events which may betarget photo events after the six dimensional target tracking detectionof the subject technology. As a result 99.994% of video data 302 wasdiscarded or otherwise rejected as background photo events.

It will be apparent to those of ordinary skill in the art that certainaspects involved in the operation of the system 100, 300, or otherdevices may be embodied in a computer program product that includes acomputer usable and/or readable medium. For example, such a computerusable medium may consist of a read only memory device, such as a CD ROMdisk or conventional ROM devices, or a random access memory, such as ahard drive device or a computer diskette, or flash memory device havinga computer readable program code stored thereon.

Elements or steps of different implementations described may be combinedto form other implementations not specifically set forth previously.Elements or steps may be left out of the systems or processes describedpreviously without adversely affecting their operation or the operationof the system in general. Furthermore, various separate elements orsteps may be combined into one or more individual elements or steps toperform the functions described in this specification.

Other implementations not specifically described in this specificationare also within the scope of the following claims.

What is claimed is:
 1. A laser detection and ranging system comprising: a receiver arranged to receive scattered laser pulses; and a processor arranged to: A. generate photo events based on the received scattered laser pulses, the photo events including target signal photo events and background photo events, B. transform the photo events into a two dimensional 2-D target tracking array including range and range-rate parameters, discard photo events determined to be background photo events, and tag photo events determined to be 2-D target signal photo events, and C. transform photo events tagged as 2-D target signal photo events into a four dimensional 4-D target tracking array including azimuth, azimuthal velocity, elevation, and elevation velocity parameters, discard photo events determined to be background photo events, and tag photo events determined to be 4-D target signal photo events.
 2. The laser detection and ranging system of claim 1, wherein the processor repeats steps B and C for a plurality of iterations, and wherein, after the first iteration, the photo events transformed in step B are the tagged 4-D target signal photo events from step C of the previous iteration.
 3. The laser detection and ranging system of claim 1, comprising storing the parameters associated with each of the tagged 4-D target signal photo events in a memory.
 4. The laser detection and ranging system of claim 3, wherein the parameters are stored in memory to represent a six dimensional (6D) array.
 5. The laser detection and ranging system of claim 4, wherein the parameters include range, range-rate, azimuth, azimuthal velocity, elevation, and elevation velocity.
 6. The laser detection and ranging system of claim 1, wherein the processor determines that photo events with a signal strength lower than a detection threshold are the background photo events.
 7. The laser detection and ranging system of claim 6, wherein the detection threshold includes a statistically significant photo event.
 8. The laser detection and ranging system of claim 6, wherein the detection threshold applied in step B is different than a detection threshold applied in step C.
 9. The laser detection and ranging system of claim 1 further comprising a laser transmitter arranged to emit laser pulses toward a target.
 10. The laser detection and ranging system of claim 1, wherein the photo events include sparse video data.
 11. A method for laser detection and ranging comprising: A. receiving scattered laser pulses B. generating photo events based on the received scattered laser pulses, the photo events including target signal photo events and background photo events, C. transforming the photo events into a two dimensional 2-D target tracking array including range and range-rate parameters, discarding photo events determined to be background photo events, and tagging photo events determined to be 2-D target signal photo events, and D. transforming photo events tagged as 2-D target signal photo events into a four dimensional (4-D) target tracking array including azimuth, azimuthal velocity, elevation, and elevation velocity parameters, discarding photo events determined to be background photo events, and tagging photo events determined to be 4-D target signal photo events.
 12. The laser detection and ranging system of claim 11, comprising repeating steps C and D for a plurality of iterations, and wherein, after the first iteration, the photo events transformed in step C are the tagged 4-D target signal photo events from step D of the previous iteration.
 13. The laser detection and ranging system of claim 11, comprising storing the parameters associated with each of the tagged 4-D target signal photo events in a memory.
 14. The laser detection and ranging system of claim 13, wherein the parameters are stored in memory to represent a six dimensional (6D) array.
 15. The laser detection and ranging system of claim 14, wherein the parameters include range, range-rate, azimuth, azimuthal velocity, elevation, and elevation velocity.
 16. The laser detection and ranging system of claim 11, discarding photo events determined to be background photo events includes comparing the photo events signal strength, wherein photo events having a signal strength lower than a detection threshold are the background photo events.
 17. The laser detection and ranging system of claim 16, wherein the detection threshold includes a statistically significant photo event.
 18. The laser detection and ranging system of claim 16, wherein the detection threshold applied in step C is different than a detection threshold applied in step D.
 19. A LADAR sparse state-space carving system comprising: a receiver arranged to receive scattered laser pulses and generate photo events based on the received scattered laser pulses, the photo events including target signal photo events and background photo events, a two dimensional 2-D target tracking detector arranged to transform the photo events into a 2-D target tracking array including range and range-rate parameters, discard photo events determined to be background photo events, and tag photo events determined to be 2-D target signal photo events, and a four dimensional 4-D target tracking detector arranged to transform photo events tagged as 2-D target signal photo events into a 4-D target tracking array including azimuth, azimuthal velocity, elevation, and elevation velocity parameters, discard photo events determined to be background photo events, and tag photo events determined to be 4-D target signal photo events.
 20. The system of claim 19, wherein the 2-D target tracking detector and the 4-D target tracking detector transform the photo events iteratively over a plurality of iterations, and wherein, after the first iteration, the photo events input into and transformed by the 2-D target tracking detector are the tagged 4-D target signal photo events from the 4-D target tracking detector during a previous iteration. 